Learning Deep Hierarchical Spatial–Spectral Features for Hyperspectral Image Classification Based on Residual 3D-2D CNN

Every pixel in a hyperspectral image contains detailed spectral information in hundreds of narrow bands captured by hyperspectral sensors. Pixel-wise classification of a hyperspectral image is the cornerstone of various hyperspectral applications. Nowadays, deep learning models represented by the convolutional neural network (CNN) provides an ideal solution for feature extraction, and has made remarkable achievements in supervised hyperspectral classification. However, hyperspectral image annotation is time-consuming and laborious, and available training data is usually limited. Due to the “small-sample problem”, CNN-based hyperspectral classification is still challenging. Focused on the limited sample-based hyperspectral classification, we designed an 11-layer CNN model called R-HybridSN (Residual-HybridSN) from the perspective of network optimization. With an organic combination of 3D-2D-CNN, residual learning, and depth-separable convolutions, R-HybridSN can better learn deep hierarchical spatial–spectral features with very few training data. The performance of R-HybridSN is evaluated over three public available hyperspectral datasets on different amounts of training samples. Using only 5%, 1%, and 1% labeled data for training in Indian Pines, Salinas, and University of Pavia, respectively, the classification accuracy of R-HybridSN is 96.46%, 98.25%, 96.59%, respectively, which is far better than the contrast models

Improving Hyperspectral Image Classification Method for Fine Land Use Assessment Application Using Semisupervised Machine Learning

Study on land use/cover can reflect changing rules of population, economy, agricultural structure adjustment, policy, and traffic and provide better service for the regional economic development and urban evolution. The study on fine land use/cover assessment using hyperspectral image classification is a focal growing area in many fields. Semisupervised learning method which takes a large number of unlabeled samples and minority labeled samples, improving classification and predicting the accuracy effectively, has been a new research direction. In this paper, we proposed improving fine land use/cover assessment based on semisupervised hyperspectral classification method. The test analysis of study area showed that the advantages of semisupervised classification method could improve the high precision overall classification and objective assessment of land use/cover results

An Improved 3D-2D Convolutional Neural Network Based on Feature Optimization for Hyperspectral Image Classification

As a new technology in the field of remote sensing, hyperspectral remote sensing has been widely used in land classification, mineral exploration, environmental monitoring, and other areas. In recent years, deep learning has achieved outstanding results in hyperspectral image classification tasks. However, problems such as low classification accuracy for small sample classes in unbalanced datasets and lack of robustness of the models usually lead to unstable classification performance of hyperspectral images. Therefore, from the perspective of feature optimization, we propose an improved hybrid convolutional neural network for hyperspectral image feature extraction and classification. Different from the current simple multi-scale feature extraction, we first optimize the features of each scale, and then perform multi-scale feature fusion. To this end, we use 3D dilated convolution to design a multi-level feature extraction block (MFB), which can be used to extract features with different correlation strengths at a fixed scale. Then, we construct a spatial multi-scale interactive attention (SMIA) module in the spatial feature enhancement phase, which can refine the multi-scale features through the attention weights of multi-scale feature interaction, and further improve the quality of spatial features. Finally, experiments were performed on different datasets, including balanced and unbalanced samples. The results show that the proposed model is more accurate and the extracted features are more robust

Assessing system impedance based on data regrouping

In recent years, assessing supply system impedance has become crucial due to the concerns on power quality and the proliferation of distributed generators. In this paper, a novel method is shown for passive measurement of system impedances using the gapless waveform data collected by a portable power quality monitoring device. This method improves the overall measurement accuracy through data regrouping. Compared with the traditional methods that use the consecutive measurement data directly, the proposed method regroups the data to find better candidates with less flotation on the system side. Simulation studies and extensive field tests have been conducted to verify the effectiveness of the proposed method. The results indicate that the proposed method can serve as a useful tool for impedance measurement tasks performed by utility companies

A New Semisupervised-Entropy Framework of Hyperspectral Image Classification Based on Random Forest

The purposes of the algorithm presented in this paper are to select features with the highest average separability by using the random forest method to distinguish categories that are easy to distinguish and to select the most divisible features from the most difficult categories using the weighted entropy algorithm. The framework is composed of five parts: (1) random samples selection with (2) probabilistic output initial random forest classification processing based on the number of votes; (3) semisupervised classification, which is an improvement of the supervision classification of random forest based on the weighted entropy algorithm; (4) precision evaluation; and (5) a comparison with the traditional minimum distance classification and the support vector machine (SVM) classification. In order to verify the universality of the proposed algorithm, two different data sources are tested, which are AVIRIS and Hyperion data. The results show that the overall classification accuracy of AVIRIS data is up to 87.36%, the kappa coefficient is up to 0.8591, and the classification time is 22.72s. Hyperion data is up to 99.17%, the kappa coefficient is up to 0.9904, and the classification time is 8.16s. Classification accuracy is obviously improved and efficiency is greatly improved, compared with the minimum distance and the SVM classifier and the CART classifier

Assessing system impedance based on data regrouping

In recent years, assessing supply system impedance has become crucial due to the concerns on power quality and the proliferation of distributed generators. In this paper, a novel method is shown for passive measurement of system impedances using the gapless waveform data collected by a portable power quality monitoring device. This method improves the overall measurement accuracy through data regrouping. Compared with the traditional methods that use the consecutive measurement data directly, the proposed method regroups the data to find better candidates with less flotation on the system side. Simulation studies and extensive field tests have been conducted to verify the effectiveness of the proposed method. The results indicate that the proposed method can serve as a useful tool for impedance measurement tasks performed by utility companies

Information Extraction Method of Alpine Glaciers with Multitemporal and Multiangle Remote Sensing

A glacier extraction method based on multitemporal and multiangle remote sensing images is proposed. Firstly, a "global-local" threshold segmentation method is applied to extract snow ice boundaries with multitemporal remote sensing images. Secondly, the glacier boundaries hidden by mountain shadows are restored with topographic features and multiangle information in different remote sensing images. Finally, the best glacier extents are the intersections of different glacier/snow extents. In order to validate the method, a glacier extraction is tested with 4 Landsat images during 2009-2010 in the western part of Tumur peak of the Tienshan Mountains. The results show that the proposed method performs well in extracting the glacier boundaries inside the mountain shadows with multiangle images